
"""simple docstring"""






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase        =				TypeVar("""T""")




def __magic_name__	(				_lowerCamelCase:		Union[str, Any]			)					->						Union[str, Any]:

	'''simple docstring'''




	return (position - 1) // 2




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						str:

	'''simple docstring'''




	return (2 * position) + 1




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						str:

	'''simple docstring'''




	return (2 * position) + 2



class 			lowercase					( Generic[T]		):






			def __init__(self  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  []
				lowerCAmelCase												=  {}
				lowerCAmelCase												=  0






			def __len__(self  :	str			)       ->	Dict:





				"""simple docstring"""


				return self.elements






			def __repr__(self  :	List[str]			)       ->	Tuple:





				"""simple docstring"""


				return str(self.heap			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	str:





				"""simple docstring"""


				return self.elements == 0






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Any      ,SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Dict:





				"""simple docstring"""


				self.heap.append((elem, weight)			)
				lowerCAmelCase												=  self.elements
				self.elements += 1
				self._bubble_up(__snake_case			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	Optional[int]:





				"""simple docstring"""


				if self.elements > 1:
					self._swap_nodes(0      ,self.elements - 1			)
				lowerCAmelCase												=  self.heap.pop()
				del self.position_map[elem]
				self.elements -= 1
				if self.elements > 0:
					lowerCAmelCase												=  self.heap[0]
					self._bubble_down(__snake_case			)
				return elem






			def 					UpperCAmelCase							(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.position_map[elem]
				lowerCAmelCase												=  (elem, weight)
				if position > 0:
					lowerCAmelCase												=  get_parent_position(__snake_case			)
					lowerCAmelCase												=  self.heap[parent_position]
					if parent_weight > weight:
						self._bubble_up(__snake_case			)
					else:
						self._bubble_down(__snake_case			)
				else:
					self._bubble_down(__snake_case			)






			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Any			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.position_map[elem]
				if curr_pos == 0:
					return None
				lowerCAmelCase												=  get_parent_position(__snake_case			)
				lowerCAmelCase												=  self.heap[curr_pos]
				lowerCAmelCase												=  self.heap[parent_position]
				if parent_weight > weight:
					self._swap_nodes(__snake_case      ,__snake_case			)
					return self._bubble_up(__snake_case			)
				return None






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.position_map[elem]
				lowerCAmelCase												=  self.heap[curr_pos]
				lowerCAmelCase												=  get_child_left_position(__snake_case			)
				lowerCAmelCase												=  get_child_right_position(__snake_case			)
				if child_left_position < self.elements and child_right_position < self.elements:
					lowerCAmelCase												=  self.heap[child_left_position]
					lowerCAmelCase												=  self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
						self._swap_nodes(__snake_case      ,__snake_case			)
						return self._bubble_down(__snake_case			)
				if child_left_position < self.elements:
					lowerCAmelCase												=  self.heap[child_left_position]
					if child_left_weight < weight:
						self._swap_nodes(__snake_case      ,__snake_case			)
						return self._bubble_down(__snake_case			)
				else:
					return None
				if child_right_position < self.elements:
					lowerCAmelCase												=  self.heap[child_right_position]
					if child_right_weight < weight:
						self._swap_nodes(__snake_case      ,__snake_case			)
						return self._bubble_down(__snake_case			)
				return None






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.heap[nodea_pos][0]
				lowerCAmelCase												=  self.heap[nodea_pos][0]
				lowerCAmelCase												=  (
				    self.heap[nodea_pos],
				    self.heap[nodea_pos],
				)
				lowerCAmelCase												=  nodea_pos
				lowerCAmelCase												=  nodea_pos





class 			lowercase					( Generic[T]		):






			def __init__(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  {}
				lowerCAmelCase												=  0






			def __repr__(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				return str(self.connections			)






			def __len__(self  :	Any			)       ->	List[str]:





				"""simple docstring"""


				return self.nodes






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]			)       ->	List[str]:





				"""simple docstring"""


				if node not in self.connections:
					lowerCAmelCase												=  {}
					self.nodes += 1






			def 					UpperCAmelCase							(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Any:





				"""simple docstring"""


				self.add_node(__snake_case			)
				self.add_node(__snake_case			)
				lowerCAmelCase												=  weight
				lowerCAmelCase												=  weight





def __magic_name__	(				_lowerCamelCase:		int,					)					->						Dict:

	'''simple docstring'''




	lowerCAmelCase												=  {node: maxsize for node in graph.connections}
	lowerCAmelCase												=  {node: None for node in graph.connections}

	lowerCAmelCase												=  MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__			)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	lowerCAmelCase												=  priority_queue.extract_min()
	lowerCAmelCase												=  0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			lowerCAmelCase												=  dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(SCREAMING_SNAKE_CASE__,					dist[neighbour]			)
			lowerCAmelCase												=  node

    # running prim's algorithm
	while not priority_queue.is_empty():
		lowerCAmelCase												=  priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				lowerCAmelCase												=  dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(SCREAMING_SNAKE_CASE__,					dist[neighbour]			)
				lowerCAmelCase												=  node
	return dist, parent






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




